72 citations · 230 across the 17 of their papers we have counts for
17 papers
ReForce: Learning Force-aware Retargeting for Dexterous Manipulation
Yuhang Wu, Lingqi Zeng, Changwei Jing +2
Human demonstrations offer a scalable data source for dexterous manipulation, but transferring them to robot actions remains challenging due to the embodiment gap. Today's retarget…
WorldString: Actionable World Representation
Kunqi Xu, Jitao Li, Jianglong Ye +4
Inspired by the emergent behaviors in large language models that generalized human intelligence, the research community is pursuing similar emergent capabilities within world model…
Cross-Hand Latent Representation for Vision-Language-Action Models
Guangqi Jiang, Yutong Liang, Jianglong Ye +6
Dexterous manipulation is essential for real-world robot autonomy, mirroring the central role of human hand coordination in daily activity. Humans rely on rich multimodal perceptio…
Contact-Aware Neural Dynamics
Changwei Jing, Jai Krishna Bandi, Jianglong Ye +4
High-fidelity physics simulation is essential for scalable robotic learning, but the sim-to-real gap persists, especially for tasks involving complex, dynamic, and discontinuous in…
From Power to Precision: Learning Fine-grained Dexterity for Multi-fingered Robotic Hands
Jianglong Ye, Lai Wei, Guangqi Jiang +3
Human grasps can be roughly categorized into two types: power grasps and precision grasps. Precision grasping enables tool use and is believed to have influenced human evolution. T…
Real Deep Research for AI, Robotics and Beyond
Xueyan Zou, Jianglong Ye, Hao Zhang +7
With the rapid growth of research in AI and robotics now producing over 10,000 papers annually it has become increasingly difficult for researchers to stay up to date. Fast evolvin…
Dex1B: Learning with 1B Demonstrations for Dexterous Manipulation
Jianglong Ye, Keyi Wang, Chengjing Yuan +6
Generating large-scale demonstrations for dexterous hand manipulation remains challenging, and several approaches have been proposed in recent years to address this. Among them, ge…
Co-Design of Soft Gripper with Neural Physics
Sha Yi, Xueqian Bai, Adabhav Singh +3
For robot manipulation, both the controller and end-effector design are crucial. Soft grippers are generalizable by deforming to different geometries, but designing such a gripper…
M3: 3D-Spatial MultiModal Memory
Xueyan Zou, Yuchen Song, Ri-Zhao Qiu +4
We present 3D Spatial MultiModal Memory (M3), a multimodal memory system designed to retain information about medium-sized static scenes through video sources for visual perception…
Learning Generalizable Feature Fields for Mobile Manipulation
Ri-Zhao Qiu, Yafei Hu, Yuchen Song +8
An open problem in mobile manipulation is how to represent objects and scenes in a unified manner so that robots can use both for navigation and manipulation. The latter requires c…
Consistent-1-to-3: Consistent Image to 3D View Synthesis via Geometry-aware Diffusion Models
Jianglong Ye, Peng Wang, Kejie Li +2
Zero-shot novel view synthesis (NVS) from a single image is an essential problem in 3D object understanding. While recent approaches that leverage pre-trained generative models can…
GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields
Yanjie Ze, Ge Yan, Yueh-Hua Wu +6
It is a long-standing problem in robotics to develop agents capable of executing diverse manipulation tasks from visual observations in unstructured real-world environments. To ach…
MVDream: Multi-view Diffusion for 3D Generation
Yichun Shi, Peng Wang, Jianglong Ye +3
We introduce MVDream, a diffusion model that is able to generate consistent multi-view images from a given text prompt. Learning from both 2D and 3D data, a multi-view diffusion mo…
FeatureNeRF: Learning Generalizable NeRFs by Distilling Foundation Models
Jianglong Ye, Naiyan Wang, Xiaolong Wang
Recent works on generalizable NeRFs have shown promising results on novel view synthesis from single or few images. However, such models have rarely been applied on other downstrea…
Learning Continuous Grasping Function with a Dexterous Hand from Human Demonstrations
Jianglong Ye, Jiashun Wang, Binghao Huang +2
We propose to learn to generate grasping motion for manipulation with a dexterous hand using implicit functions. With continuous time inputs, the model can generate a continuous an…
GIFS: Neural Implicit Function for General Shape Representation
Jianglong Ye, Yuntao Chen, Naiyan Wang +1
Recent development of neural implicit function has shown tremendous success on high-quality 3D shape reconstruction. However, most works divide the space into inside and outside of…
Online Adaptation for Implicit Object Tracking and Shape Reconstruction in the Wild
Jianglong Ye, Yuntao Chen, Naiyan Wang +1
Tracking and reconstructing 3D objects from cluttered scenes are the key components for computer vision, robotics and autonomous driving systems. While recent progress in implicit…